From Ternary Modeling to Predictive PROTAC Design: A Computational Perspective

Abstract

Proteolysis-targeting chimeras (PROTACs) are heterobifunctional small molecules that induce targeted protein degradation by recruiting an E3 ligase to a protein of interest. Since 2019, publication volume has accelerated, and computational methods have expanded from isolated demonstrations into practical tools for modeling PROTAC-induced ternary complexes, designing linkers, and forecasting degradation-related outcomes. Here, we present a Perspective on computational PROTAC methodologies published from 2019 to the present, organizing the field into two complementary streams: (i) constraint-driven, physics-based workflows that assemble and refine ternary complex models by enforcing geometric feasibility and evaluating pose stability using docking and molecular simulation; and (ii) data-driven workflows, including deep learning predictors and generative models that predict ternary complex structure, degradation end points, or linker chemistry from structural and assay data. We highlight representative approaches spanning restrained/tethered docking, MD-based refinement and dynamic stability scoring, coarse-grained free-energy modeling, SE(3)/E(3)-equivariant structure prediction, supervised degradation efficacy prediction, and generative linker design. We close by emphasizing persistent gaps, fragmented benchmarking, score robustness across targets and E3 ligases, and nonstandard molecular representations that currently limit generalization and reproducible, pipeline-ready deployment.